375 Identification of QTL associated with antibody response to common infectious diseases in commercial sows
Bibliographic record
Abstract
Abstract The objective of this study was to perform genome-wide association studies (GWAS) to identify Quantitative Trait Loci (QTL) associated with antibody response to infectious diseases in commercial sows. A total of 2,848 Large White x Landrace replacement gilts were sourced from 17 high-health multipliers (7 breeding companies; BC) and introduced to 23 commercial farms with a history of common pig diseases, following the standard acclimation procedures with an average of 53 animals per entry group (CG). Serum was used to quantify antibody response to swine influenza virus (SIV), Mycoplasma hyopneumoniae (MH), porcine circovirus type-2 (PCV2), and 8 serotypes of Actinobacillus pleuropneumoniae (APP1-3, 5, 7, 10, 12, and 13) at entry (S/PEntry), following acclimation (S/PAcclimation), and during parities 1 (S/PParity1) and 2 (S/PParity2). All animals were genotyped for 38,191 SNPs. GWAS was performed using BayesB (pi=0.99), with the fixed effect of CG and the random effects of SNPs in the model. For APP, QTL were only identified at S/PAcclimation; on SSC14 (2Mb) for APP3, APP7, APP10, and APP13 that explained 5.6, 4.7, 2.8, and 3.6% of the genetic variance, respectively. A gene within this QTL region is SYK, involved in the control of immune-receptors. For APP5, a QTL that explained 4.2% of the genetic variance was identified on SSC4 (105Mb), which co-localizes with two genes associated with immune-response: SIKE1and NRAS. For SIV, no QTL was identified. A QTL on SSC7 (130-131Mb) was identified for MH (S/PParity1, 5.1%) and PCV2 (S/PEntry, 34%; S/PAcclimation, 43.4%). These results provide new information on the genetic basis of response to infectious diseases in sows. The identified QTL have the potential to be used to select for improved immune response. The authors thanks PigGen Canada, Genome Canada, and the Canadian Swine Health Board for financial support, and the late Dr. Stephen Bishop for his scientific contributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".